Evaluation of a geomorphic instream flow tool for conducting hydraulic‐habitat modelling
Bibliographic record
Abstract
Abstract Conventional hydraulic‐habitat modelling methods are time‐consuming to implement. In response to repeated calls for more efficient and practical approaches, researchers have developed a geomorphic instream‐flow tool (GIFT) that combines a method to simulate reach‐averaged hydraulics at flows less than bankfull and depth and velocity frequency distributions to develop streamflow‐fish habitat relationships. This approach requires fewer resources to implement than conventional methods, but it has not been widely adopted because it has been subject to minimal testing and validation. This study evaluates the performance of GIFT by comparing its outputs to empirical measurements and conventional model outputs from eight rivers in western North America. The results of this comparison indicate that the root mean square errors for average depth and velocity were 0.078 m and 0.047 m/s, respectively, and the fit of modelled depth and velocity frequency distributions was satisfactory (index of agreement >0.9) for 11 of 15 surveys for depth and 12 of 15 surveys for velocity. GIFT‐derived fish habitat‐streamflow relationships peaked at lower flows than benchmark relationships in smaller streams (mean annual discharge [MAD] < 0.15 m 3 /s) and are markedly differed from the benchmark in the largest river (MAD of 87 m 3 /s). GIFT was also paired with a geomorphic regime model to predict the direction of changes in channel morphology and fish habitat following forest harvesting in one watershed. GIFT provides an alternative to conventional modelling approaches for single‐thread, gravel‐bed rivers with a MAD of around 15 m 3 /s or less. Application of this technique outside of these bounds, or in other regions should proceed with caution, as these scenarios have not been tested.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".